Chris J. Maddison
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I am an Assistant Professor at the University of Toronto and a Canada CIFAR Chair in AI at the Vector Institute. I work on the methodology of machine learning, with an emphasis on algorithms that work at scale in deep learning applications. I am particularly interested in methods for Bayesian inference and optimization. So far I have worked on gradient estimation, variational inference, Monte Carlo methods, and first-order methods for optimization. Here is a short bio in the third-person.
InterviewsCuriosity and my sense of beauty are some of the reasons that I study machine learning. If you are interested in this or some other scattered thoughts, here are some interviews.
- A Recipe for Creativity, IAS, 2020.
- On unsupervised learning, moments of surprise, and becoming unstuck, IAS, 2019.
- The centre of the AI universe, CIFAR News, 2019.
- Google DeepMind's AlphaGo, U of T News, 2016.
Department of Computer Science University of Toronto 6 King's College Rd. Toronto, Ontario
E-mail: cmaddis [at] cs [dot] toronto [dot] edu